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Open Access Publications from the University of California

Scholarly Works (6 results)

  • Article
Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health, Volume 23, Issue 4.1 (2022)

Learning Objective: To better understand the nature of support offered through residency peer support programs and to explore trainee perceptions of the benefits, potential harms, and optimal characteristics of peer support.

Background: Though peer support groups are often utilized during residency training, the dynamics, content, and impact of social support offered through peer support are poorly understood.

Objective: To explore trainee perceptions of the benefits, drawbacks, and optimal membership and facilitation of peer support groups.

Methods: After engaging in a peer support program at an emergency medicine residency program, fifteen residents and four group facilitators participated in four focus groups in 2018. Interview questions explored the dynamics of group interactions, types of support offered, and psychological impacts of participation. The authors conducted a reflexive thematic analysis of data, performing iterative coding and organization of interview transcripts.

Results: Discussions with experienced senior residents and alumni normalized residents’ workplace struggles and provided them with insights into the trajectory of their residency experiences. Vulnerable group dialogue was enhanced by the use of “insider” participants, however participants acknowledged the potential contributions of mental health professionals. Though groups occasionally utilized maladaptive coping strategies and lacked actual solutions, they also enhanced residents’ sense of belonging, willingness to share personal struggles, and ability to “reset” in the clinical environment. Results of our reflexive thematic analysis are described with representative quotes in Table 1.

Conclusions: Participants offered insights into the benefits and drawbacks of peer support as well as optimal peer group composition and facilitation. Support groups may be more effective if they engage a complementary model of alumni and psychologist facilitators, avoid fatalism, and aim to foster intimate connections among residents. These findings can inform the development of future initiatives aiming to create a safe space for trainees to discuss workplace stressors.

    Cover page: “Everybody in this room can understand”: A Qualitative Exploration of Peer Support during Residency TrainingCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2022)
    The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the application of supervised learning in genomics research. However, the assumptions behind the statistical models and performance evaluations in ML software frequently are not met in biological systems. In this Review, we illustrate the impact of several common pitfalls encountered when applying supervised ML in genomics. We explore how the structure of genomics data can bias performance evaluations and predictions. To address the challenges associated with applying cutting-edge ML methods to genomics, we describe solutions and appropriate use cases where ML modelling shows great potential.
      Cover page: Navigating the pitfalls of applying machine learning in genomics
      • Article
      • Peer Reviewed
      UC Santa Cruz Previously Published Works (2013)
      Cytosine, 5-methylcytosine, and 5-hydroxymethylcytosine were identified during translocation of single DNA template strands through a modified Mycobacterium smegmatis porin A (M2MspA) nanopore under control of phi29 DNA polymerase. This identification was based on three consecutive ionic current states that correspond to passage of modified or unmodified CG dinucleotides and their immediate neighbors through the nanopore limiting aperture. To establish quality scores for these calls, we examined ~3,300 translocation events for 48 distinct DNA constructs. Each experiment analyzed a mixture of cytosine-, 5-methylcytosine-, and 5-hydroxymethylcytosine-bearing DNA strands that contained a marker that independently established the correct cytosine methylation status at the target CG of each molecule tested. To calculate error rates for these calls, we established decision boundaries using a variety of machine-learning methods. These error rates depended upon the identity of the bases immediately 5' and 3' of the targeted CG dinucleotide, and ranged from 1.7% to 12.2% for a single-pass read. We estimate that Q40 values (0.01% error rates) for methylation status calls could be achieved by reading single molecules 5-19 times depending upon sequence context.
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2019)
        Non-homologous end-joining (NHEJ) plays an important role in double-strand break (DSB) repair of DNA. Recent studies have shown that the error patterns of NHEJ are strongly biased by sequence context, but these studies were based on relatively few templates. To investigate this more thoroughly, we systematically profiled ∼1.16 million independent mutational events resulting from CRISPR/Cas9-mediated cleavage and NHEJ-mediated DSB repair of 6872 synthetic target sequences, introduced into a human cell line via lentiviral infection. We find that: (i) insertions are dominated by 1 bp events templated by sequence immediately upstream of the cleavage site, (ii) deletions are predominantly associated with microhomology and (iii) targets exhibit variable but reproducible diversity with respect to the number and relative frequency of the mutational outcomes to which they give rise. From these data, we trained a model that uses local sequence context to predict the distribution of mutational outcomes. Exploiting the bias of NHEJ outcomes towards microhomology mediated events, we demonstrate the programming of deletion patterns by introducing microhomology to specific locations in the vicinity of the DSB site. We anticipate that our results will inform investigations of DSB repair mechanisms as well as the design of CRISPR/Cas9 experiments for diverse applications including genome-wide screens, gene therapy, lineage tracing and molecular recording.
          Cover page: Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2019)
          Over one million candidate regulatory elements have been identified across the human genome, but nearly all are unvalidated and their target genes uncertain. Approaches based on human genetics are limited in scope to common variants and in resolution by linkage disequilibrium. We present a multiplex, expression quantitative trait locus (eQTL)-inspired framework for mapping enhancer-gene pairs by introducing random combinations of CRISPR/Cas9-mediated perturbations to each of many cells, followed by single-cell RNA sequencing (RNA-seq). Across two experiments, we used dCas9-KRAB to perturb 5,920 candidate enhancers with no strong a priori hypothesis as to their target gene(s), measuring effects by profiling 254,974 single-cell transcriptomes. We identified 664 (470 high-confidence) cis enhancer-gene pairs, which were enriched for specific transcription factors, non-housekeeping status, and genomic and 3D conformational proximity to their target genes. This framework will facilitate the large-scale mapping of enhancer-gene regulatory interactions, a critical yet largely uncharted component of the cis-regulatory landscape of the human genome.
            Cover page: A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2023)
            Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
              Cover page: The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.
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